Course Descriptions

MAGY.6213I Elemnts Of Real Analy I
3 Points, Mondays, 5:007:30PM, Gaoyong Zhang
Description TBA 
MAGY.7033I Linear Algebra I
3 Points, Wednesdays, 5:007:30PM, Yisong Yang
Description TBA 
MATHGA.1410001 Introduction To Math Analysis I
3 Points, Mondays, 5:107:00PM, Sinan Gunturk
Elements of topology on the real line. Rigorous treatment of limits, continuity, differentiation, and the Riemann integral. Taylor series. Introduction to metric spaces. Pointwise and uniform convergence for sequences and series of functions. Applications.
Recitation/ Problem Session: Monday, 7:10pm8:30pm (following the course)

MATHGA.1410002 Intro To Math Analysis I Recitation
3 Points, Mondays, 7:108:30PM, TBA
Description TBA 
MATHGA.2010001 Numerical Methods I
3 Points, Mondays, 5:107:00PM, Michael Overton
Prerequisites:
A good background in linear algebra, and some experience with writing computer programs (in MATLAB, Python or another language). MATLAB will be used as the main language for the course. Alternatively, you can also use Python for the homework assignments. You are encouraged but not required to learn and use a compiled language.
Description:
This course is part of a twocourse series meant to introduce graduate students in mathematics to the fundamentals of numerical mathematics (but any Ph.D. student seriously interested in applied mathematics should take it). It will be a demanding course covering a broad range of topics. There will be extensive homework assignments involving a mix of theory and computational experiments, and an inclass final. Topics covered in the class include floatingpoint arithmetic, solving large linear systems, eigenvalue problems, interpolation and quadrature (approximation theory), nonlinear systems of equations, linear and nonlinear least squares, nonlinear optimization, and Fourier transforms. This course will not cover differential equations, which form the core of the second part of this series, Numerical Methods II.
Recommended Text (Springer books are available online from the NYU network):
 Deuflhard, P. & Hohmann, A. (2003). Numerical Analysis in Modern Scientific Computing. Texts in Applied Mathematiks [Series, Bk. 43]. New York, NY: SpringerVerlag.
Further Reading (available on reserve at the Courant Library):
 Bau III, D., & Trefethen, L.N. (1997). Numerical Linear Algebra. Philadelphia, PA: Society for Industrial & Applied Mathematics.
 Quarteroni, A., Sacco, R., & Saleri, F. (2006). Numerical Mathematics (2^{nd} ed.). Texts in Applied Mathematics [Series, Bk. 37]. New York, NY: SpringerVerlag.
If you want to brush up your MATLAB:
 Gander, W., Gander, M.J., & Kwok, F. (2014). Scientific Computing – An Introduction Using Maple and MATLAB. Texts in Computation Science and Engineering [Series, Vol. 11]. New York, NY: SpringerVerlag.
 Moler, C. (2004). Numerical Computing with Matlab. SIAM. Available online.

MATHGA.2011001 Advanced Topics In Numerical Methods: Monte Carlo Methods
3 Points, Wednesdays, 5:107:00PM, Jonathan Goodman
Description TBA 
MATHGA.2011002 Advanced Topics In Numerical Analysis: Numerical Optimization
3 Points, Mondays, 5:107:00PM, Margaret Wright
Description TBA 
MATHGA.2011003 Advanced Topics In Numerical Analysis: Fast Solvers
3 Points, Wednesdays, 11:0012:50PM, Michael O'Neil
Prerequisites: Graduatelevel proficiency in linear algebra, undergraduatelevel proficiency in numerical methods and PDEs.
Description: The numerical solution of elliptic PDEs is a key topic in applied and computational mathematics, having applications in classic fields such as electromagnetics and fluid dynamics. Furthermore, elliptic solves are often the most timeconsuming piece of numerical time marching schemes for parabolic problems. Numerical discretization of the differential formulation of these problems using finite elements or finite differences lead to sparse linear systems, while discretization of equivalent integral formulations of the same problem lead to dense linear systems. Despite these differences, many of the same hierarchical linear algebraic ideas can lead to fast solvers ( O(N) or O(N log N) ) in both situations. This course will discuss the necessary tools from numerical linear algebra and hierarchical matrix compression and inversion, as well as cover the mathematical underpinnings of why these algorithms are even possible in the first place.
Text: P.G. Martinsson, Fast Direct Solvers for Elliptic PDEs, SIAM Books, Philadelphia, PA, (c) 2020. 
MATHGA.2041001 Computing In Finance
3 Points, Thursdays, 7:109:00PM, Eran Fishler and Lee Maclin
Prerequisites: Procedural programming, some knowledge of Java recommended.
Description: This course will introduce students to the software development process, including applications in financial asset trading, research, hedging, portfolio management, and risk management. Students will use the Java programming language to develop objectoriented software, and will focus on the most broadly important elements of programming  superior design, effective problem solving, and the proper use of data structures and algorithms. Students will work with market and historical data to run simulations and test strategies. The course is designed to give students a feel for the practical considerations of software development and deployment. Several key technologies and recent innovations in financial computing will be presented and discussed. 
MATHGA.2043001 Scientific Computing
3 Points, Thursdays, 5:107:00PM, Aleksandar Donev
Prerequisites: Undergraduate multivariate calculus and linear algebra. Programming experience strongly recommended but not required.
Overview: This course is intended to provide a practical introduction to computational problem solving. Topics covered include: the notion of wellconditioned and poorly conditioned problems, with examples drawn from linear algebra; the concepts of forward and backward stability of an algorithm, with examples drawn from floating point arithmetic and linearalgebra; basic techniques for the numerical solution of linear and nonlinear equations, and for numerical optimization, with examples taken from linear algebra and linear programming; principles of numerical interpolation, differentiation and integration, with examples such as splines and quadrature schemes; an introduction to numerical methods for solving ordinary differential equations, with examples such as multistep, Runge Kutta and collocation methods, along with a basic introduction of concepts such as convergence and linear stability; An introduction to basic matrix factorizations, such as the SVD; techniques for computing matrix factorizations, with examples such as the QR method for finding eigenvectors; Basic principles of the discrete/fast Fourier transform, with applications to signal processing, data compression and the solution of differential equations.
This is not a programming course but programming in homework projects with MATLAB/Octave and/or C is an important part of the course work. As many of the class handouts are in the form of MATLAB/Octave scripts, students are strongly encouraged to obtain access to and familiarize themselves with these programming environments.
Recommended Texts:
 Bau III, D., & Trefethen, L.N. (1997). Numerical Linear Algebra. Philadelphia, PA: Society for Industrial & Applied Mathematics
 Quarteroni, A.M., & Saleri, F. (2006). Texts in Computational Science & Engineering [Series, Bk. 2]. Scientific Computing with MATLAB and Octave (2^{nd} ed.). New York, NY: SpringerVerlag
 Otto, S.R., & Denier, J.P. (2005). An Introduction to Programming and Numerical Methods in MATLAB. London: SpringerVerlag London

MATHGA.2045001 Nonlinear Problems In Finance: Models And Computational Methods
3 Points, Wednesdays, 7:109:00PM, Julien Guyon and Bryan Liang
Prerequisites: Continuous Time Finance or permission of instructor.
Description: The classical curriculum of mathematical finance programs generally covers the link between linear parabolic partial differential equations (PDEs) and stochastic differential equations (SDEs), resulting from FeynmamKac's formula. However, the challenges faced by today's practitioners mostly involve nonlinear PDEs. The aim of this course is to provide the students with the mathematical tools and computational methods required to tackle these issues, and illustrate the methods with practical case studies such as American option pricing, uncertain volatility, uncertain mortality, different rates for borrowing and lending, calibration of models to market smiles, credit valuation adjustment (CVA), portfolio optimization, transaction costs, illiquid markets, superreplication under delta and gamma constraints, etc.
We will strive to make this course reasonably comprehensive, and to find the right balance between ideas, mathematical theory, and numerical implementations. We will spend some time on the theory: optimal stopping, stochastic control, backward stochastic differential equations (BSDEs), McKean SDEs, branching diffusions. But the main focus will deliberately be on ideas and numerical examples, which we believe help a lot in understanding the tools and building intuition.
Recommended text:
 Guyon, J. and HenryLabordère, P.: Nonlinear Option Pricing, Chapman & Hall/CRC Financial Mathematics Series, 2014.

MATHGA.2046001 Advanced Statistical Inference And Machine Learning
3 Points, Wednesdays, 5:107:00PM, Gordon Ritter
Prerequisites: Financial Securities and Markets; Risk & Portfolio Management; and Computing in Finance, or equivalent programming experience.
Description: A rigorous background in Bayesian statistics geared towards applications in finance, including decision theory and the Bayesian approach to modeling, inference, point estimation, and forecasting, sufficient statistics, exponential families and conjugate priors, and the posterior predictive density. A detailed treatment of multivariate regression including Bayesian regression, variable selection techniques, multilevel/hierarchical regression models, and generalized linear models (GLMs). Inference for classical timeseries models, state estimation and parameter learning in Hidden Markov Models (HMMs) including the Kalman filter, the BaumWelch algorithm and more generally, Bayesian networks and belief propagation. Solution techniques including Markov Chain Monte Carlo methods, Gibbs Sampling, the EM algorithm, and variational mean field. Real world examples drawn from finance to include stochastic volatility models, portfolio optimization with transaction costs, risk models, and multivariate forecasting. 
MATHGA.2047001 Data Science In Quantitative Finance
3 Points, Tuesdays, 7:109:00PM, Petter Kolm and Ivailo Dimov
Prerequisites: Risk & Portfolio Management; Scientific Computing in Finance (or Scientific Computing); and Computing in Finance, or equivalent programming experience.
Description: This is a full semester course focusing on practical aspects of alternative data, machine learning and data science in quantitative finance. Homework and handson projects form an integral part of the course, where students get to explore realworld datasets and software.
The course begins with an overview of the field, its technological and mathematical foundations, paying special attention to differences between data science in finance and other industries. We review the software that will be used throughout the course.
We examine the basic problems of supervised and unsupervised machine learning and learn the link between regression and conditioning. Then we deepen our understanding of the main challenge in data science – the curse of dimensionality – as well as the basic tradeoff of variance (model parsimony) vs. bias (model flexibility).
Demonstrations are given for real world data sets and basic data acquisition techniques such as web scraping and the merging of data sets. As homework each student is assigned to take part in downloading, cleaning, and testing data in a common repository, to be used at later stages in the class.
We examine linear and quadratic methods in regression, classification and unsupervised learning. We build a BARRAstyle implicit riskfactor model and examine predictive models for countylevel real estate, economic and demographic data, and macroeconomic data. We then take a dive into PCA, ICA and clustering methods to develop global macro indicators and estimate stable correlation matrices for equities.
In many reallife problems, one needs to do SVD on a matrix with missing values. Common applications include noisy imagerecognition and recommendation systems. We discuss the Expectation Maximization algorithm, the L1regularized Compressed Sensing algorithm, and a naïve gradient search algorithm.
The rest of the course focuses on nonlinear or highdimensional supervised learning problems. First, kernel smoothing and regression methods are introduced as a way to tackle nonlinear problems in low dimensions in a nearly modelfree way. Then we proceed to generalize the kernel regression method in the Bayesian Regression framework of Gaussian Fields, and for classification as we introduce Support Vector Machines, Random Forest regression, Neural Nets and Universal Function Approximators. 
MATHGA.2049001 Alternative Data In Quantitative Finance (2nd Half Of Semester)
1.5 Points, Thursdays, 7:109:00PM, Gene Ekster
Prerequisites: Risk and Portfolio Management; and Computing in Finance. In addition, students should have a working knowledge of statistics, finance, and basic machine learning. Students should have working experience with the Python stack (numpy/pandas/scikitlearn).
Description: This halfsemester elective course examines techniques dealing with the challenges of the alternative data ecosystem in quantitative and fundamental investment processes. We will address the quantitative tools and technique for alternative data including identifier mapping, stable panel creation, dataset evaluation and sensitive information extraction. We will go through the quantitative process of transferring raw data into investment data and tradable signals using text mining, time series analysis and machine learning. It is important that students taking this course have working experience with Python Stack. We will analyze realworld datasets and model them in Python using techniques from statistics, quantitative finance and machine learning. 
MATHGA.2070001 Data Science And DataDriven Modeling (1st Half Of Semester)
1.5 Points, Tuesdays, 7:109:00PM, Ivailo Dimov and Petter Kolm
This is a halfsemester course covering practical aspects of econometrics/statistics and data science/machine learning in an integrated and unified way as they are applied in the financial industry. We examine statistical inference for linear models, supervised learning (Lasso, ridge and elasticnet), and unsupervised learning (PCA and SVDbased) machine learning techniques, applying these to solve common problems in finance. In addition, we cover model selection via crossvalidation; manipulating, merging and cleaning large datasets in Python; and webscraping of publicly available data.

MATHGA.2110001 Linear Algebra I
3 Points, Tuesdays, 5:107:00PM, Yu Chen
Prerequisites: Undergraduate linear algebra or permission of the instructor.
Description: Linear spaces, subspaces. Linear dependence, linear independence; span, basis, dimension, isomorphism. Quotient spaces. Linear functionals, Dual spaces. Linear mappings, null space, range, fundamental theorem of linear algebra. Underdetermined systems of linear equations. Composition, inverse, transpose of linear maps, algebra of linear maps. Similarity transformations. Matrices, matrix Multiplication, Matrix Inverse, Matrix Representation of Linear Maps, Determinant, Laplace expansion, Cramer's rule. Eigenvalue problem, eigenvalues and eigenvectors, characteristic polynomial, CayleyHamilton theorem. Diagonalization.
Text: Friedberg, S.H., Insel, A.J., & Spence, L.E. (2003). Linear Algebra (4^{th} ed.). Upper Saddle River, NJ: PrenticeHall/ Pearson Education.
Recommended Text: Lipschutz, S., & Lipson, M. (2012). Schaum’s Outlines [Series]. Schaum’s Outline of Linear Algebra (5^{th} ed.). New York, NY: McGrawHill.
Note: Extensive lecture notes keyed to these texts will be issued by the instructor.

MATHGA.2111001 Linear Algebra (OneTerm)
3 Points, Thursdays, 9:0010:50AM, Dimitris Giannakis
Prerequisites: Undergraduate linear algebra.
Description: Linear algebra is two things in one: a general methodology for solving linear systems, and a beautiful abstract structure underlying much of mathematics and the sciences. This course will try to strike a balance between both. We will follow the book of our own Peter Lax, which does a superb job in describing the mathematical structure of linear algebra, and complement it with applications and computing. The most advanced topics include spectral theory, convexity, duality, and various matrix decompositions.
Text: Lax, P.D. (2007). Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts [Series, Bk. 78]. Linear Algebra and Its Applications (2^{nd} ed.). Hoboken, NJ: John Wiley & Sons/ WileyInterscience.
Recommended Text: Strang, G. (2005). Linear Algebra and Its Applications (4^{th} ed.). Stamford, CT: Cengage Learning.

MATHGA.2130001 Algebra I
3 Points, Thursdays, 7:109:00PM, Alena Pirutka
Prerequisites: Elements of linear algebra and the theory of rings and fields.
Description: Basic concepts of groups, rings and fields. Symmetry groups, linear groups, Sylow theorems; quotient rings, polynomial rings, ideals, unique factorization, Nullstellensatz; field extensions, finite fields.
Recommended Texts:
 Artin, M. (2010). Featured Titles for Abstract Algebra [Series]. Algebra (2^{nd} ed.). Upper Saddle River, NJ: Pearson
 ChambertLoir, A. (2004). Undergraduate Texts in Mathematics [Series]. A Field Guide to Algebra (2005 ed.). New York, NY: SpringerVerlag
 Serre, JP. (1996). Graduate Texts in Mathematics [Series, Vol. 7]. A Course in Arithmetic (Corr. 3^{rd} printing 1996 ed.). New York, NY: SpringerVerlag

MATHGA.2310001 Topology I
3 Points, Thursdays, 5:107:00PM, Sylvain Cappell
Prerequisites: Any knowledge of groups, rings, vector spaces and multivariable calculus is helpful. Undergraduate students planning to take this course must have V63.0343 Algebra I or permission of the Department.
Description: After introducing metric and general topological spaces, the emphasis will be on the algebraic topology of manifolds and cell complexes. Elements of algebraic topology to be covered include fundamental groups and covering spaces, homotopy and the degree of maps and its applications. Some differential topology will be introduced including transversality and intersection theory. Some examples will be taken from knot theory.
Recommended Texts:
 Hatcher, A. (2002). Algebraic Topology. New York, NY: Cambridge University Press
 Munkres, J. (2000). Topology (2^{nd} ed.). Upper Saddle River, NJ: PrenticeHall/ Pearson Education
 Guillemin, V., Pollack, A. (1974). Differential Topology. Englewood Cliffs, NJ: PrenticeHall
 Milnor, J.W. (1997). Princeton Landmarks in Mathematics [Series]. Topology from a Differentiable Viewpoint (Rev. ed.). Princeton, NJ: Princeton University Press

MATHGA.2350001 Differential Geometry I
3 Points, Tuesdays, 3:205:10PM, Jeff Cheeger
Prerequisites: Multivariable calculus and linear algebra.
Description: Differentiable manifolds, tangent bundle, embedding theorems, vector fields and differential forms. Introduction to Riemannian metrics, connections and geodesics.
Text: Lee, J.M. (2009). Graduate Studies in Mathematics [Series, Vol. 107]. Manifolds and Differential Geometry. Providence, RI: American Mathematical Society.

MATHGA.2400002 Advanced Topics In Geometry: Topology And Combinatorics
3 Points, Wednesdays, 1:253:15PM, Mikhael Gromov
The course will include the following topics:
 Elementary introduction to the homology theory and its geometric corollaries, such as the BorsukUlam (ham sandwich) theorem.
 Topological proofs of some combinatorial problems, such as the Kneser Conjecture, Necklace splitting problem(s) and overlaps inequalities for high dimensional expanders.
 Applications of combinatorial inequalities from the extremal set theory, such as the KrushkalKatona theorem, to algebraic topology.
Basic References:
 Using the BorsukUlam Theorem Lectures on Topological Methods in Combinatorics and Geometry, book by Jiri Matousek.
 From combinatorics to topology via algebraic isoperimetry (article in Geom. Funct. Anal. Vol. 20 (2010) 416526) by M.Gromov

MATHGA.2420001 Advanced Topics Mathematics: Working Group In Modeling And Simulation
3 Points, Thursdays, 12:302:00PM, Aleksandar Donev and Miranda HolmesCerfon and Leif Ristroph
As part of our new NSF research training group (RTG) in Modeling & Simulation, we will be organizing a lunchtime group meeting for students, postdocs, and faculty working in applied mathematics who do modeling & simulation. The aim is to create a space to discuss applied mathematics research in an informal setting: to (a) give students and postdocs a chance to present their research (or a topic of common interest) and get feedback from the group, (b) learn about other ongoing and future research activities in applied math at the Institute, and (c) discuss important open problems and research challenges.
The meetings will be Thursdays from 12:302:00, in room 1314; the weekly schedule is posted here.

MATHGA.2420002 Advanced Topics: Seminar In AOS
3 Points, Fridays, 3:455:00PM, Shafer Smith
Description: The Atmosphere Ocean Science Student Seminar focuses on research and presentation skills. The course is spread across two semesters, and participants are expected to participate in both to earn the full 3 credits. Participants will prepare and present a full length (4550 minute) talk on their research each semester, for a total of two over the duration of the course. In addition, short “elevator talks” are developed and given in the second semester, the goal being to encapsulate the key points of your research in under 5 minutes. A main goal of the course is learning to present your research to different audiences. We consider overview talks, appropriate for a department wide colloquium, specialty talks, as would be given in a focused seminar, and a broad pitch you would give when meeting people and entering the job market. When not presenting, students are expected to engage with the speaker, asking questions and providing feedback at the end of the talk.

MATHGA.2430001 Real Variables (OneTerm)
3 Points, Mondays, Wednesdays, 9:3510:50AM, Raghu Varadhan
Note: Master's students need permission of course instructor before registering for this course.
Prerequisites: A familiarity with rigorous mathematics, proof writing, and the epsilondelta approach to analysis, preferably at the level of MATHGA 1410, 1420 Introduction to Mathematical Analysis I, II.
Description: Measure theory and integration. Lebesgue measure on the line and abstract measure spaces. Absolute continuity, Lebesgue differentiation, and the RadonNikodym theorem. Product measures, the Fubini theorem, etc. L^{p} spaces, Hilbert spaces, and the Riesz representation theorem. Fourier series.
Main Text: Folland's Real Analysis: Modern Techniques and Their Applications
Secondary Text: Bass' Real Analysis for Graduate Students

MATHGA.2450001 Complex Variables I
3 Points, Tuesdays, 7:109:00PM, TBA
Prerequisites: Advanced calculus (or equivalent).
Description: Complex numbers; analytic functions, CauchyRiemann equations; linear fractional transformations; construction and geometry of the elementary functions; Green's theorem, Cauchy's theorem; Jordan curve theorem, Cauchy's formula; Taylor's theorem, Laurent expansion; analytic continuation; isolated singularities, Liouville's theorem; Abel's convergence theorem and the Poisson integral formula.
Text: Brown, J., & Churchill, R. (2008). Complex Variables and Applications (8^{th} ed.). New York, NY: McGrawHill.

MATHGA.2451001 Complex Variables (OneTerm)
3 Points, Tuesdays, Thursdays, 2:003:15PM, Gerard Ben Arous
Note: Master's students need permission of course instructor before registering for this course.
Prerequisites: Complex Variables I (or equivalent) and MATHGA 1410 Introduction to Math Analysis I.
Description: Complex numbers, the complex plane. Power series, differentiability of convergent power series. CauchyRiemann equations, harmonic functions. conformal mapping, linear fractional transformation. Integration, Cauchy integral theorem, Cauchy integral formula. Morera's theorem. Taylor series, residue calculus. Maximum modulus theorem. Poisson formula. Liouville theorem. Rouche's theorem. Weierstrass and MittagLeffler representation theorems. Singularities of analytic functions, poles, branch points, essential singularities, branch points. Analytic continuation, monodromy theorem, Schwarz reflection principle. Compactness of families of uniformly bounded analytic functions. Integral representations of special functions. Distribution of function values of entire functions.
Text: Ahlfors, L. (1979). International Series in Pure and Applied Mathematics [Series, Bk. 7]. Complex Analysis (4thin ed.). New York, NY: McGrawHill.

MATHGA.2490001 Introduction To Partial Differential Equations
3 Points, Mondays, 11:0012:50PM, Esteban Tabak
Note: Master's students should consult course instructor before registering for PDE II in the spring.
Prerequisites: Knowledge of undergraduate level linear algebra and ODE; also some exposure to complex variables (can be taken concurrently).
Description: A basic introduction to PDEs, designed for a broad range of students whose goals may range from theory to applications. This course emphasizes examples, representation formulas, and properties that can be understood using relatively elementary tools. We will take a broad viewpoint, including how the equations we consider emerge from applications, and how they can be solved numerically. Topics will include: the heat equation; the wave equation; Laplace's equation; conservation laws; and HamiltonJacobi equations. Methods introduced through these topics will include: fundamental solutions and Green's functions; energy principles; maximum principles; separation of variables; Duhamel's principle; the method of characteristics; numerical schemes involving finite differences or Galerkin approximation; and many more.
See the syllabus for more information (including a tentative semester plan).
Recommended Texts:
 Guenther, R.B., & Lee, J.W. (1996). Partial Differential Equations of Mathematical Physics and Integral Equations. Mineola, NY: Dover Publications.
 Evans, L.C. (2010). Graduate Studies in Mathematics [Series, Bk. 19]. Partial Differential Equations (2^{nd} ed.). Providence, RI: American Mathematical Society.

MATHGA.2510001 Advanced Partial Differential Equations
3 Points, Thursdays, 9:0010:50AM, Robert Kohn
Elliptic regularity theory: Cacciopoli inequality, Schauder estimates, De GiorgiNash Theory. Variational methods. Homogenization. Basics on hyperbolic equations, dispersive equations and viscosity solutions.

MATHGA.2563001 Harmonic Analysis
3 Points, Wednesdays, 3:205:10PM, Sylvia Serfaty
Prerequisites:
Real analysis; basic knowledge of complex variables and functional analysis.
Description:
Classical Fourier Analysis: Fourier series on the circle, Fourier transform on the Euclidean space, Introduction to Fourier transform on LCA groups. Stationary phase. Topics in real variable methods: maximal functions, Hilbert and Riesz transforms and singular integral operators. Time permitting: Introduction to LittlewoodPaley theory, timefrequency analysis, and wavelet theory.
Recommended Text:
Muscalu, C. and Schlag, W. (2013). Cambridge Studies in Advanced Mathematics[Series, Bk. 137]. Classical and Multilinear Harmonic Analysis (Vol.1). New York, NY: Cambridge University Press. (Online version available to NYU users through Cambridge University Press.

MATHGA.2610002 Advanced Topics In PDE: Geometric Variational Problems
3 Points, Thursdays, 11:0012:50PM, Fanghua Lin and Fengbo Hang
Prerequisites : Real Variables, Sobolev Spaces and Basic PDEs
Description: The topics to be discussed in the first part of the course are: harmonic maps in 2D including the Riemann mappings, univalent harmonic maps and its Jacobian, and some applications to isometric embeddings and minimal surfaces. A few basic Sobolev inequalities and facts about Sobolev maps will be proved too.
For the second part of the course, we start with Polyakov formula in the spectral geometry as a motivation for MoserTrudinger inequality, then it follows by Szego limit theorem on the circle. Afterwards, we switch to more recent progress of generalization to the 2sphere and its close relation to minimal nodes problem in numerical analysis. We shall briefly discuss sphere covering inequality and applications to mean field equations. Various open problem will be remarked. 
MATHGA.2650001 Advanced Topics In Analysis: Derivation Of Kinetic Models From Hamiltonian Dynamics
3 Points, Thursdays, 3:205:10PM, Pierre Germain
Description: The aim of this course is to examine the derivation of kinetic models, more precisely collisional kinetic models, from Hamiltonian dynamics. This includes the linear Boltzmann Equation for particles (classical or quantum) propagating in a random environment, and the nonlinear Boltzmann Equation (classical or quantum) for manybody problems. These questions have seen spectacular progress in the last decades, but they remain mostly mysterious; I will try and present the state of the art.

MATHGA.2701001 Methods Of Applied Math
3 Points, Mondays, 1:253:15PM, Oliver Buhler
Prerequisites: Elementary linear algebra and differential equations.
Description: This is a firstyear course for all incoming PhD and Masters students interested in pursuing research in applied mathematics. It provides a concise and selfcontained introduction to advanced mathematical methods, especially in the asymptotic analysis of differential equations. Topics include scaling, perturbation methods, multiscale asymptotics, transform methods, geometric wave theory, and calculus of variations
Recommended Texts:
 Barenblatt, G.I. (1996). Cambridge Texts in Applied Mathematics [Series, Bk. 14]. Scaling, Selfsimilarity, and Intermediate Asymptotics: Dimensional Analysis and Intermediate Asymptotics. New York, NY: Cambridge University Press
 Hinch, E.J. (1991). Cambridge Texts in Applied Mathematics [Series, Bk. 6]. Perturbation Methods. New York, NY: Cambridge University Press
 Bender, C.M., & Orszag, S.A. (1999). Advanced Mathematical Methods for Scientists and Engineers [Series, Vol. 1]. Asymptotic Methods and Perturbation Theory. New York, NY: SpringerVerlag
 Whitham, G.B. (1999). Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts [Series Bk. 42]. Linear and Nonlinear Waves (Reprint ed.). New York, NY: John Wiley & Sons/ WileyInterscience
 Gelfand, I.M., & Fomin, S.V. (2000). Calculus of Variations. Mineola, NY: Dover Publications

MATHGA.2702001 Fluid Dynamics
3 Points, Wednesdays, 1:253:15PM, Michael Shelley
Prerequisites: Introductory complex variable and partial differential equations.
Description: The course will expose students to basic fluid dynamics from a mathematical and physical perspectives, covering both compressible and incompressible flows. Topics: conservation of mass, momentum, and Energy. Eulerian and Lagrangian formulations. Basic theory of inviscid incompressible and compressible fluids, including the formation of shock waves. Kinematics and dynamics of vorticity and circulation. Special solutions to the Euler equations: potential flows, rotational flows, irrotational flows and conformal mapping methods. The NavierStokes equations, boundary conditions, boundary layer theory. The Stokes Equations.
Text: Childress, S. Courant Lecture Notes in Mathematics [Series, Bk. 19]. An Introduction to Theoretical Fluid Mechanics. Providence, RI: American Mathematical Society/ Courant Institute of Mathematical Sciences.
Recommended Text: Acheson, D.J. (1990). Oxford Applied Mathematics & Computing Science Series [Series]. Elementary Fluid Dynamics. New York, NY: Oxford University Press.

MATHGA.2707001 Time Series Analysis & Statistical Arbitrage
3 Points, Mondays, 5:107:00PM, Farshid Asl and Robert Reider
Prerequisites: Financial Securities and Markets; Scientific Computing in Finance (or Scientific Computing); and familiarity with basic probability.
Description: The term "statistical arbitrage" covers any trading strategy that uses statistical tools and time series analysis to identify approximate arbitrage opportunities while evaluating the risks inherent in the trades (considering the transaction costs and other practical aspects). This course starts with a review of Time Series models and addresses econometric aspects of financial markets such as volatility and correlation models. We will review several stochastic volatility models and their estimation and calibration techniques as well as their applications in volatility based trading strategies. We will then focus on statistical arbitrage trading strategies based on cointegration, and review pairs trading strategies. We will present several key concepts of market microstructure, including models of market impact, which will be discussed in the context of developing strategies for optimal execution. We will also present practical constraints in trading strategies and further practical issues in simulation techniques. Finally, we will review several algorithmic trading strategies frequently used by practitioners. 
MATHGA.2751001 Risk & Portfolio Management
3 Points, Tuesdays, 7:109:00PM, Kenneth Winston
Prerequisites: Multivariate calculus, linear algebra, and calculusbased probability.
Description: Risk management is arguably one of the most important tools for managing investment portfolios and trading books and quantifying the effects of leverage and diversification (or lack thereof).
This course is an introduction to portfolio and risk management techniques for portfolios of (i) equities, delta1 securities, and futures and (ii) basic fixed income securities.
A systematic approach to the subject is adopted, based on selection of risk factors, econometric analysis, extremevalue theory for tail estimation, correlation analysis, and copulas to estimate joint factor distributions. We will cover the construction of risk measures (e.g. VaR and Expected Shortfall) and portfolios (e.g. portfolio optimization and risk). As part of the course, we review current risk models and practices used by large financial institutions.
It is important that students taking this course have good working knowledge of multivariate calculus, linear algebra and calculusbased probability. 
MATHGA.2755001 Project & Presentation
3 Points, Thursdays, 5:107:00PM, Petter Kolm
Students in the Mathematics in Finance program conduct research projects individually or in small groups under the supervision of finance professionals. The course culminates in oral and written presentations of the research results.

MATHGA.2791001 Financial Securities And Markets
3 Points, Wednesdays, 7:109:00PM, Marco Avellaneda
Prerequisites: Multivariate calculus, linear algebra, and calculusbased probability.
This course provides a quantitative introduction to financial securities for students who are aspiring to careers in the financial industry. We study how securities traded, priced and hedged in the financial markets. Topics include: arbitrage; riskneutral valuation; the lognormal hypothesis; binomial trees; the BlackScholes formula and applications; the BlackScholes partial differential equation; American options; onefactor interest rate models; swaps, caps, floors, swaptions, and other interestbased derivatives; credit risk and credit derivatives; clearing; valuation adjustment and capital requirements. It is important that students taking this course have good working knowledge of multivariate calculus, linear algebra and calculusbased probability.

MATHGA.2792001 Continuous Time Finance
3 Points, Mondays, 7:109:00PM, Alireza Javaheri and Samim Ghamami
Prerequisites: Financial Securities and Markets; and Stochastic Calculus, or equivalent.
Description: A second course in arbitragebased pricing of derivative securities. The BlackScholes model and its generalizations: equivalent martingale measures; the martingale representation theorem; the market price of risk; applications including change of numeraire and the analysis of quantos. Interest rate models: the HeathJarrowMorton approach and its relation to short rate models; applications including mortgagebacked securities. The volatility smile/skew and approaches to accounting for it: underlyings with jumps, local volatility models, and stochastic volatility models. 
MATHGA.2803001 Fixed Income Derivatives: Models & Strategies In Practice (1st Half Of Semester)
1.5 Points, Thursdays, 7:109:00PM, Leon Tatevossian and Amir Sadr
Prerequisites: Computing in Finance, or equivalent programming skills; and Financial Securities and Markets, or equivalent familiarity with BlackScholes interest rate models.
Description: This halfsemester class focuses on the practical workings of the fixedincome and ratesderivatives markets. The course content is motivated by a representative set of realworld trading, investment, and hedging objectives. Each situation will be examined from the ground level and its risk and reward attributes will be identified. This will enable the students to understand the link from the underlying market views to the applicable product set and the tools for managing the position once it is implemented. Common threads among products – structural or modelbased – will be emphasized. We plan on covering bonds, swaps, flow options, semiexotics, and some structured products.
A problemoriented holistic view of the ratederivatives market is a natural way to understand the line from product creation to modeling, marketing, trading, and hedging. The instructors hope to convey their intuition about both the power and limitations of models and show how sellside practitioners manage these constraints in the context of changes in market backdrop, customer demands, and trading parameters. 
MATHGA.2805001 Trends In SellSide Modeling: Xva, Capital And Credit Derivatives
3 Points, Tuesdays, 5:107:00PM, Leif Andersen
Prerequisites: Advanced Risk Management; Financial Securities and Markets, or equivalent familiarity with market and credit risk models; and Computing in Finance, or equivalent programming experience.
Description: This class explores technical and regulatory aspects of counterparty credit risk, with an emphasis on model building and computational methods. The first part of the class will provide technical foundation, including the mathematical tools needed to define and compute valuation adjustments such as CVA and DVA. The second part of the class will move from pricing to regulation, with an emphasis on the computational aspects of regulatory credit risk capital under Basel 3. A variety of highly topical subjects will be discussed during the course, including: funding costs, XVA metrics, initial margin, credit risk mitigation, central clearing, and balance sheet management. Students will get to build a realistic computer system for counterparty risk management of collateralized fixed income portfolios, and will be exposed to modern frameworks for interest rate simulation and capital management. 
MATHGA.2830002 Advanced Topics In Applied Math: Modeling And Statistical Prediction Of Complex Systems
3 Points, Thursdays, 9:0010:50AM, Andrew Majda
Complex systems are ubiquitous in science and engineering and are characterized by a large dimensional phase space and a large dimension of strong instabilities which transfer energy throughout the system.
Typical examples can be found in the atmosphere and ocean science, plasma physics, neural and material sciences. Modeling and statistical prediction of typical phenomena such as extreme events and anomalous transport have important scientific significance and large societal impacts.
Key mathematical issues in the complex systems are their basic mathematical structural properties and qualitative features, their statistical prediction and uncertainty quantification (UQ), their data assimilation, and coping with the inevitable model errors that arise in approximating such complex systems. These model errors arise through both the curse of small ensemble size and a lack of physical understanding. Efficient and accurate algorithms for parameter estimation, data assimilation, and prediction are required using cheap stochastic parameterizations, judicious linear feedback control, and a new nonlinear modeling framework, which provide accurate predictions of both the observed and hidden extreme events, as well as the strongly nonGaussian statistics in highly intermittent nonlinear models.
This is a research expository course on the development of new mathematical framework of building suitable nonlinear approximate models, which aim at predicting both the observed and hidden statistics in complex nonlinear dynamical systems for short, medium, and long range forecasting using only short and partially observed training time series. The applied mathematics of various complex dynamical systems are studied through the paradigm of modern applied mathematics involving the blending of rigorous mathematical theory, qualitative and quantitative modeling, and novel numerical procedures driven by the goal of understanding physical phenomena which are of central importance. The contents include the general mathematical framework and theory, instructive qualitative models, and concrete models from climate atmosphere ocean science and plasma physics as well as laboratory water wave experiments. Also recent mathematical strategies for turbulent dynamical systems as well as rigorous results are briefly surveyed. Accessible open problems are often mentioned.
Audience: The course should be interesting for graduate students, and postdocs in pure and applied mathematics, physics, engineering, and climate, atmosphere, ocean science interested in turbulent dynamical systems as well as other complex systems. 
MATHGA.2855001 Advanced Topics In Mathematical Physiology: Modeling Of Neuronal Dynamics
3 Points, Wednesdays, 1:253:15PM, John Rinzel
Prerequisite: Some familiarity with applied differential equations (seek consent of instructor if in doubt).
Description: This course will focus on neurophysiology and biophysics at the cellular level  the mechanistic and mathematical descriptions of neuronal dynamics and input/output properties – some linear and many quite nonlinear. How do neurons integrate synaptic inputs over their dendrites, generate spikes and transmit spikes to targets? With a range of different intrinsic properties neurons can temporally organize their distinctive spiking signatures to perform as integrators, as differentiators, as fast or slow pacemakers, or as bursters. Neuronal functional architecture varies; different branching structures and ionic channel distributions allow for local processing in dendrites or more feedforward transmission to the soma; coupling between soma and axon can shape spike train output. Excitability and propagation will be described with HodgkinHuxleylike models and reductions; synaptic transduction will feature ionic channel kinetics, dynamics of depression/facilitation/plasticity and control at dendritic spines. Both numerical simulation and analytical techniques (perturbation and bifurcation methods) will be described and used, serving as an applied introduction to these methodologies. Students will undertake computing projects related to the course material. 
MATHGA.2901001 Essentials Of Probability
3 Points, Wednesdays, 5:107:00PM, Richard Kleeman
Prerequisites:
Calculus through partial derivatives and multiple integrals; no previous knowledge of probability is required.
Description:
The course introduces the basic concepts and methods of probability.
Topics include: probability spaces, random variables, distributions, law of large numbers, central limit theorem, random walk, Markov chains and martingales in discrete time, and if time allows diffusion processes including Brownian motion.
Recommeded text:
Probability Essentials, by J.Jacod and P.Protter. Springer, 2004.

MATHGA.2902002 Stochastic Calculus Optional Problem Session
3 Points, Thursdays, 5:307:00PM, TBA
Description TBA 
MATHGA.2903001 Stochatic Calculus (2nd Half Of Semester)
1.5 Points, Mondays, 7:109:00PM, Jonathan Goodman
Prerequisite: Multivariate calculus, linear algebra, and calculusbased probability.
Description: The goal of this halfsemester course is for students to develop an understanding of the techniques of stochastic processes and stochastic calculus as it is applied in financial applications. We begin by constructing the Brownian motion (BM) and the Ito integral, studying their properties. Then we turn to Ito’s lemma and Girsanov’s theorem, covering several practical applications. Towards the end of the course, we study the linkage between SDEs and PDEs through the FeynmanKac equation. It is important that students taking this course have good working knowledge of calculusbased probability. 
MATHGA.2911001 Probability Theory I
3 Points, Tuesdays, 11:0012:50PM, Eyal Lubetzky
Prerequisites:
A first course in probability, familiarity with Lebesgue integral, or MATHGA 2430 Real Variables as mandatory corequisite.
Description:
First semester in an annual sequence of Probability Theory, aimed primarily for Ph.D. students. Topics include laws of large numbers, weak convergence, central limit theorems, conditional expectation, martingales and Markov chains.
Recommended Text:
S.R.S. Varadhan, Probability Theory (2001).

MATHGA.2931002 Advanced Topics In Probability: Multiplicative Chaos, From Gff To Polymers (Sept 8 To Oct 15)
3 Points, Tuesdays, Thursdays, 1:253:15PM, Ofer Zeitouni
Prerequisites: Graduate course in Probability
Description: We will discuss the construction and approximations of Gaussian (and nonGaussian) multiplicative chaos, and their role in the analysis of logarithmicaly correlated Gaussian fields, random matrices, and polymers.
Texts: There will be no textbook. I will rely on articles from the current literature, and probably will also distribute notes. 
MATHGA.3001001 Geophysical Fluid Dynamics
3 Points, Tuesdays, 9:0010:50AM, Olivier Pauluis
Description:
This course serves as an introduction to the fundamentals of geophysical fluid dynamics. No prior knowledge of fluid dynamics will be assumed, but the course will move quickly into the subtopic of rapidly rotating, stratified flows. Topics to be covered include (but are not limited to): the advective derivative, momentum conservation and continuity, the rotating NavierStokes equations and nondimensional parameters, equations of state and thermodynamics of Newtonian fluids, atmospheric and oceanic basic states, the fundamental balances (thermal wind, geostrophic and hydrostatic), the rotating shallow water model, vorticity and potential vorticity, inertiagravity waves, geostrophic adjustment, the quasigeostrophic approximation and other smallRossby number limits, Rossby waves, baroclinic and barotropic instabilities, Rayleigh and CharneyStern theorems, geostrophic turbulence. Students will be assigned biweekly homework assignments and some computer exercises, and will be expected to complete a final project. This course will be supplemented with outofclass instruction.
Recommended Texts:
 Vallis, G.K. (2006). Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Largescale Circulation. New York, NY: Cambridge University Press.
 Salmon, R. (1998). Lectures on Geophysical Fluid Dynamics. New York, NY: Oxford University Press.
 Pedlosky, J. (1992). Geophysical Fluid Dynamics (2nd ed.). New York, NY: SpringerVerlag.

MATHGA.3010001 Advanced Topics In AOS: Climate Change
3 Points, Tuesdays, 11:0012:50PM, Edwin Gerber
After starting off with the warmest January in recorded history, 2020 is on pace to be the warmest year ever observed. The last 6 years (20142019) are the 6 warmest years in recorded history. It is likely the hottest the Earth has ever been since the last interglacial period 125,000 years ago.
The first predictions of human induced global warming were made over a century ago, but the topic remains controversial despite the fact that the world has warmed 1 degree Celcius over the intervening years. In this course, we will investigate observational evidence as well as the physical and mathematical foundations upon which forecasts of future climate are based. What are the key uncertainties in the predictions, and what steps are required to reduce them? We will find that it is not the science of global warming that is controversial, but rather, what to do about it.
By reading through a mixture of historic and current studies, investigating key processes that affect the sensitivity of our planet to greenhouse gases, and exploring a hierarchy of climate models, this course will get you up to speed on the science of climate change. Grades will be based on a course project using a climate model to predict the impact of anthropogenic forcing on the Earth's climate. Particularly attention will be paid to establishing reproducible science and quantifying the uncertainty in your prediction.
A background in atmosphere or ocean science is plus, but the course will be structured so that anyone with a reasonable background in mathematics and physics can participate.